MATLAB Code Implementation for Monte Carlo Simulation

This source code implements Monte Carlo simulation, a probabilistic numerical computation method widely applied in finance, physics, biology, and engineering fields. The code demonstrates a complete Monte Carlo workflow incorporating key components: random variate generation using MATLAB's statistical functions, probability distribution modeling, iterative sampling algorithms, and automated result visualization through plotting commands. For implementation, the code typically utilizes MATLAB's built-in functions like rand/randn for random number generation, statistical toolboxes for probability calculations, and vectorized operations for efficient large-scale simulations. Through studying this code, MATLAB beginners can gain practical understanding of core Monte Carlo concepts including probability density functions, convergence analysis, and error estimation techniques. The implementation also helps develop essential MATLAB programming skills such as loop optimization, data structure handling, and graphical output generation, fostering good coding practices for computational research projects.